import numpy as np

class ByzantineRobustAggregator:
    def __init__(self, tau=10.0, beta=0.8):
        self.tau = tau  # Temperature coefficient
        self.beta = beta  # Suppression factor
    
    def dynamic_weighting(self, reputations: dict, gradients: list) -> dict:
        """
        Trust-based weight initialization (Algorithm 2 Steps 1-4)
        Args:
            reputations: {node_id: R_i}
            gradients: List of model gradients
        Returns:
            Normalized aggregation weights
        """
        # Trust-based weights
        exp_trust = {nid: np.exp(rep/self.tau) for nid, rep in reputations.items()}
        sum_exp = sum(exp_trust.values())
        weights = {nid: exp/sum_exp for nid, exp in exp_trust.items()}
        
        # Optional: Gradient deviation analysis (Steps 7-13)
        if gradients:
            # Implement median gradient deviation suppression
            pass
        return weights
    
    def robust_aggregate(self, local_models: list, weights: dict) -> dict:
        """
        Trust-reweighted model aggregation (Step 23)
        Args:
            local_models: List of model state_dicts
            weights: Aggregation weights per node
        Returns:
            Global model parameters
        """
        global_model = {}
        for key in local_models[0].keys():
            # Weighted sum of parameters
            global_model[key] = sum(w * local_models[i][key] 
                                 for i, w in enumerate(weights.values()))
        return global_model
